Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates
Abstract
In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates, constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics.
Cite
Text
Nejati et al. "Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.Markdown
[Nejati et al. "Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates." Proceedings of The 4th Annual Learning for Dynamics and Control Conference, 2022.](https://mlanthology.org/l4dc/2022/nejati2022l4dc-datadriven/)BibTeX
@inproceedings{nejati2022l4dc-datadriven,
title = {{Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates}},
author = {Nejati, Ameneh and Zhong, Bingzhuo and Caccamo, Marco and Zamani, Majid},
booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference},
year = {2022},
pages = {763-776},
volume = {168},
url = {https://mlanthology.org/l4dc/2022/nejati2022l4dc-datadriven/}
}